Decision Concept Lattice vs. Decision Trees and Random Forests

• URL: http://arxiv.org/abs/2106.00387v1
• Date: Tue, 1 Jun 2021 10:45:35 GMT
• Title: Decision Concept Lattice vs. Decision Trees and Random Forests
• Authors: Egor Dudyrev, Sergei O. Kuznetsov
• Abstract summary: We merge the ideas underlying decision trees, their ensembles and FCA by proposing a new supervised machine learning model. Specifically, we first propose a state-time algorithm for constructing a part of the concept lattice that is based on a decision tree. Second, we describe a prediction scheme based on a concept lattice for solving both classification and regression problems.
• Score: 4.898744396854312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision trees and their ensembles are very popular models of supervised machine learning. In this paper we merge the ideas underlying decision trees, their ensembles and FCA by proposing a new supervised machine learning model which can be constructed in polynomial time and is applicable for both classification and regression problems. Specifically, we first propose a polynomial-time algorithm for constructing a part of the concept lattice that is based on a decision tree. Second, we describe a prediction scheme based on a concept lattice for solving both classification and regression tasks with prediction quality comparable to that of state-of-the-art models.

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